Abstract Chronically implanted microelectrode arrays (MEAs) for recording extracellular neural activity are central to scientific studies of neural circuit function in behaving animals. These studies seek to understand how neurons encode information and how neural signals can be decoded to provide insights into brain learning and dysfunction. The majority of microelectrode MEAs, and especially those available commercially, are fabricated from silicon and leverage techniques associated with integrated circuit microfabrication and packaging. For microelectrode MEAs, the major limitations for chronic neural recording are the reactive tissue response that encapsulates electrodes and kills or damages neurons in the vicinity of the electrode and the degradation and failure of materials used in MEA fabrication. An effective means of minimizing the foreign body response is the use of ultramicroelectrode MEAs (UMEAs) with subcellular cross-sectional dimensions. In related work, we have demonstrated that carbon-fiber ultramicroelectrodes substantially evade a foreign body response and have been shown to provide stable chronic neural recordings in small-animal models. However, a scalable manufacturing process for carbon-fiber ultramicroelectrodes has not emerged. The proposed effort is aimed at developing and demonstrating the chronic stability and reliability of ultramicroelectrodes based on amorphous silicon carbide (a- SiC) UMEAs that are fabricated by industry-standard thin-film processes. We aim to develop a fabrication process for a-SiC UMEAs with 32 to 128 ultramicroelectrodes and demonstrate the stability of these UMEAs through accelerated laboratory testing and their functionality by neural recording and histology using chronic implants in rat cortex and in a 3-4 year non-human primate study. To facilitate dissemination of the a-SiC UMEA technology, electrical interconnect hardware and implantation methods will also be developed. We anticipate the proposed a-SiC UMEAs impacting the neuroscience community by providing a highly stable neural interface that allows single-unit and ensemble recording for probing neuronal circuitry on a dimensional scale that is not possible with current multielectrode recording devices. We expect a-SiC UMEAs will provide new insights into the neural networks and changes in neural circuitry that may accompany behavior and adaption.